Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 26, 20 December 2023
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Bitcoin price and return prediction based on LSTM
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Abstract
This paper focuses on the prediction of Bitcoin prices and returns based on the Long Short Term Memory (LSTM) neural network model, to better consider the impact of time factors. Since Bitcoin has long dominated the digital currency trading market, many researchers have completed many Bitcoin prediction results, including the screening of optimal features, comparison of prediction models and classification of prediction problems. Based on previous work, this article adds a Bitcoin revenue forecast section, presenting the results in the form of charts and data to provide more intuitive trends and more accurate performance. This paper uses LSTM as the experimental model, and uses the Bitcoin transaction history data set with timestamps as the original input. After a specific normalization method, the original model is trained, and then the subsequent transaction data is predicted. Compare it with the real value in the data set to get the final experimental results show that in this prediction problem, the performance of LSTM is slightly better than Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost); on the other hand, compared with price prediction based on real values for prediction, the prediction fluctuations of return are more obvious and more realistic, providing better reference value.
Keywords
Bitcoin price, deep learning, prediction, feature
References
1. Jang, H. Lee, J. 2017, An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. (Ieee Access, vol. 6), pp. 5427-5437.
2. Velankar, S. Valecha S. Maji S. 2018, Bitcoin price prediction using machine learning. (In 2018 20th International Conference on Advanced Communication Technology ICACT, IEEE), pp. 144-147.
3. CJi S. Kim J. Im, H. 2019, A comparative study of bitcoin price prediction using deep learning. (Mathematics, vol. 7), no. 10, pp. 898.
4. Chen, Z. Li, C. Sun, W. 2020, Bitcoin price prediction using machine learning: An approach to sample dimension engineering, (Journal of Computational and Applied Mathematics, vol. 365), pp. 112395.
5. Staudemeyer, R. C. Morris, E. R. (2019). Understanding LSTM--a tutorial into long short-term memory recurrent neural networks. arXiv preprint arXiv:1909.09586.
6. Shumway, R H. Stoffer, D S. Shumway, R H. Stoffer, D S. 2017, Time series analysis and its applications: with R examples, (ARIMA models), pp. 75-163.
7. Chen, T. He, T. Benesty, M. Khotilovich, V. Tang, Y. Cho, H. 2015, Xgboost: extreme gradient boosting. (R package version , vol. 1), no. 4, pp. 1-4.
8. Marmolin, H. 1986, Subjective MSE measures. (IEEE transactions on systems, man, and cybernetics, vol. 16), no. 3, pp. 486-489.
9. Chai, T. Draxler, R R. 2014, Root mean square error (RMSE) or mean absolute error (MAE). (Geoscientific model development discussions, vol. 7), no. 1, pp. 1525-1534.
10. Sbrana, A., Rossi, A. L. D. Naldi, M. C. (2020, December). N-BEATS-RNN: deep learning for time series forecasting. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 765-768.
11. Salinas, D. Flunkert, V. Gasthaus, J. Januschowski, T. 2020. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. (International Journal of Forecasting, vol. 36), no. 3, pp. 1181-1191.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-235-0
- ISBN (Online)
- 978-1-83558-236-7
- Published Date
- 20 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/26/20241021
- Copyright
- 20 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited